Characterization of neurophysiologic and neurocognitive biomarkers for use in genomic and clinical outcome studies of schizophrenia

Abstract

Background: Endophenotypes are quantitative, laboratory-based measures representing intermediate links in the pathways between genetic variation and the clinical expression of a disorder. Ideal endophenotypes exhibit deficits in patients, are stable over time and across shifts in psychopathology, and are suitable for repeat testing. Unfortunately, many leading candidate endophenotypes in schizophrenia have not been fully characterized simultaneously in large cohorts of patients and controls across these properties. The objectives of this study were to characterize the extent to which widely-used neurophysiological and neurocognitive endophenotypes are: 1) associated with schizophrenia, 2) stable over time, independent of state-related changes, and 3) free of potential practice/maturation or differential attrition effects in schizophrenia patients (SZ) and nonpsychiatric comparison subjects (NCS). Stability of clinical and functional measures was also assessed.

Methods: Participants (SZ n = 341; NCS n = 205) completed a battery of neurophysiological (MMN, P3a, P50 and N100 indices, PPI, startle habituation, antisaccade), neurocognitive (WRAT-3 Reading, LNS-forward, LNS-reorder, WCST-64, CVLT-II). In addition, patients were rated on clinical symptom severity as well as functional capacity and status measures (GAF, UPSA, SOF). 223 subjects (SZ n = 163; NCS n = 58) returned for retesting after 1 year.

Results: Most neurophysiological and neurocognitive measures exhibited medium-to-large deficits in schizophrenia, moderate-to-substantial stability across the retest interval, and were independent of fluctuations in clinical status. Clinical symptoms and functional measures also exhibited substantial stability. A Longitudinal Endophenotype Ranking System (LERS) was created to rank neurophysiological and neurocognitive biomarkers according to their effect sizes across endophenotype criteria.

Conclusions: The majority of neurophysiological and neurocognitive measures exhibited deficits in patients, stability over a 1-year interval and did not demonstrate practice or time effects supporting their use as endophenotypes in neural substrate and genomic studies. These measures hold promise for informing the "gene-to-phene gap" in schizophrenia research.

Figure 1. Schizophrenia patient study enrollment and reasons for not being retested.

Figure 2. Deficits in schizophrenia patients across measures.

Effect sizes (Cohen’s d) calculated from group main effects (Table 2) collapsed across time.

Figure 3. Changes in measures over 1 year retest interval in schizophrenia patients.

Effect sizes (Cohen’s d) of changes in neurocognitive and neurophysiological measures across the retest interval.

Figure 4. One year stability of candidate neurocognitive and neurophysiological endophenotypes.

Intraclass correlation coefficients are shown for schizophrenia patients (blue; n = 163) and nonpsychiatric comparison subjects (red, n = 58). The mean retest interval was 364.57 (SD: 23.83) days.

Figure 5. One year stability of clinical and functional measures in schizophrenia patients.

Figure 6. Summary of results: Longitudinal Endophenotype Ranking System LERS): Ranking biomarkers for use as endophenotypes in genomic studies and as biomarkers in clinical research studies.

Neurophysiologic and neurocognitive measures are ranked based on the observed magnitude of deficits (1–4), test-retest reliability (1–5), and state independence (1–4) as shown in Figures 2, 3, 4, 5 and described in the results.